Presentation
EGMA: Enhancing Data Reuse and Workload Balancing in Message Passing GNN Acceleration via Gram Matrix Optimization
DescriptionMessage Passing-based Graph Neural Networks (GNNs) have been widely used to analyze graph data, in which complex vertex and edge operations are performed via the exchange of information between connected vertices. Such complex GNN operations are highly dependent on the graph structure and can no longer characterized as general sparse-dense or matrix multiplications. Consequently, current data reuse and workload balance optimizations have limited applicability to Message Passing-based GNN acceleration. In this paper, we leverage the mathematical insights from Gram Matrix to simultaneously exploit data reuse and workload balancing opportunities for GNN accelerations. Upon this, we further propose a novel accelerator shortly termed as EGMA that can efficiently facilitate a wide range of GNN models with much-improved data reuse and workload balance. Consequently, EGMA can achieve performance speedup by 1.57×, 1.72×, and 1.43× and energy reduction by 38.19%, 34.02%, and 24.54% on average compared to Betty, FlowGNN, and ReGNN, respectively.
Event Type
Research Manuscript
TimeThursday, June 2710:45am - 11:00am PDT
Location3010, 3rd Floor
AI
Design
AI/ML Architecture Design